{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tushare as ts\n",
    "%matplotlib inline   \n",
    "\n",
    "# 无视warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 正常显示画图时出现的中文和负号\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif']=['SimHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "#起始和结束日期可以自行输入，否则使用默认\n",
    "def get_data(code, start_date, end_date):\n",
    "    # 配置 tushare token\n",
    "    my_token = '039660346bc8f4ad98c1b7b28fbbbb7750a6c6eea62573ff80a42593'\n",
    "    pro = ts.pro_api(my_token)\n",
    "\n",
    "    df = pro.daily(ts_code=code, start_date=start_date, end_date=end_date)\n",
    "    df.index = pd.to_datetime(df.trade_date)\n",
    "\n",
    "    return df.close\n",
    "\n",
    "#以上证综指、贵州茅台、工商银行、中国平安为例\n",
    "stocks={\n",
    "    '600519.SH':'贵州茅台',\n",
    "    '601398.SH':'工商银行',\n",
    "    '601318.SH':'中国平安'\n",
    "\n",
    "}\n",
    "\n",
    "df = pd.DataFrame()\n",
    "for code,name in stocks.items():\n",
    "    df[name] = get_data(code, '20180101', '20221231')\n",
    "\n",
    "# 按照日期正序\n",
    "df = df.sort_index()\n",
    "\n",
    "# 本地读入沪深300合并\n",
    "df_base = pd.read_csv('/Users/jin/Downloads/000300.XSHG_2018_2022.csv')\n",
    "df_base.index = pd.to_datetime(df_base.trade_date)\n",
    "df['沪深300'] = df_base['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trade_date'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '股价净值走势')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7feed15a1040>,\n",
       "  <matplotlib.axis.XTick at 0x7feed1595fd0>,\n",
       "  <matplotlib.axis.XTick at 0x7feed15e2730>,\n",
       "  <matplotlib.axis.XTick at 0x7feed16154f0>,\n",
       "  <matplotlib.axis.XTick at 0x7feed1615a00>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以第一交易日2018年1月1日收盘价为基点，计算净值并绘制净值曲线\n",
    "df_worth = df / df.iloc[0]\n",
    "df_worth.plot(figsize=(15,6))\n",
    "plt.title('股价净值走势', fontsize=10)\n",
    "plt.xticks(pd.date_range('20180101','20221231',freq='Y'),fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>累计收益率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>1.453648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工商银行</th>\n",
       "      <td>-0.297735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国平安</th>\n",
       "      <td>-0.352528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沪深300</th>\n",
       "      <td>-0.052789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          累计收益率\n",
       "贵州茅台   1.453648\n",
       "工商银行  -0.297735\n",
       "中国平安  -0.352528\n",
       "沪深300 -0.052789"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年化收益率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>0.131870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工商银行</th>\n",
       "      <td>-0.047607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国平安</th>\n",
       "      <td>-0.058225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沪深300</th>\n",
       "      <td>-0.007457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          年化收益率\n",
       "贵州茅台   0.131870\n",
       "工商银行  -0.047607\n",
       "中国平安  -0.058225\n",
       "沪深300 -0.007457"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 区间累计收益率(绝对收益率)\n",
    "total_return = df_worth.iloc[-1]-1\n",
    "total_return = pd.DataFrame(total_return.values,columns=['累计收益率'],index=total_return.index)\n",
    "total_return\n",
    "\n",
    "# 年化收益率\n",
    "annual_return = pd.DataFrame((1 + total_return.values) ** (252 / 1826) - 1,columns=['年化收益率'],index=total_return.index)\n",
    "annual_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>波动率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>0.267357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工商银行</th>\n",
       "      <td>0.148130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国平安</th>\n",
       "      <td>0.233149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沪深300</th>\n",
       "      <td>0.168623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            波动率\n",
       "贵州茅台   0.267357\n",
       "工商银行   0.148130\n",
       "中国平安   0.233149\n",
       "沪深300  0.168623"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_return = df / df.shift(1) - 1\n",
    "df_return = ((df_return.iloc[1:] - df_return.mean()) ** 2)\n",
    "\n",
    "volatility = pd.DataFrame(np.sqrt(df_return.sum() * 252 / (1826-1)),columns=['波动率'],index=total_return.index)\n",
    "volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>最大回撤</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>0.4810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工商银行</th>\n",
       "      <td>0.4761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国平安</th>\n",
       "      <td>0.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沪深300</th>\n",
       "      <td>0.3959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         最大回撤\n",
       "贵州茅台   0.4810\n",
       "工商银行   0.4761\n",
       "中国平安   0.6129\n",
       "沪深300  0.3959"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def max_drawdown_cal(df):\n",
    "    md = ((df.cummax() - df)/df.cummax()).max()\n",
    "    return round(md, 4)\n",
    "\n",
    "max_drawdown = {}\n",
    "\n",
    "stocks={\n",
    "    '600519.SH':'贵州茅台',\n",
    "    '601398.SH':'工商银行',\n",
    "    '601318.SH':'中国平安',\n",
    "    '000300.XSHG': '沪深300'\n",
    "}\n",
    "\n",
    "for code,name in stocks.items():\n",
    "    max_drawdown[name]=max_drawdown_cal(df[name])\n",
    "\n",
    "max_drawdown = pd.DataFrame(max_drawdown,index=['最大回撤']).T\n",
    "max_drawdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>0.227</td>\n",
       "      <td>1.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工商银行</th>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国平安</th>\n",
       "      <td>-0.059</td>\n",
       "      <td>1.012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      alpha   beta\n",
       "贵州茅台  0.227  1.097\n",
       "工商银行 -0.061  0.424\n",
       "中国平安 -0.059  1.012"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "#计算每日收益率 收盘价缺失值（停牌），使用前值代替\n",
    "rets=(df.iloc[:,:4].fillna(method='pad')).apply(lambda x:x/x.shift(1)-1)[1:]\n",
    "\n",
    "#市场指数为x，个股收益率为y\n",
    "x = rets.iloc[:,3].values\n",
    "y = rets.iloc[:,:3].values\n",
    "capm = pd.DataFrame()\n",
    "alpha = []\n",
    "beta = []\n",
    "for i in range(3):\n",
    "    b, a, r_value, p_value, std_err=stats.linregress(x,y[:,i])\n",
    "    #alpha转化为年化\n",
    "    alpha.append(round(a*250,3))\n",
    "    beta.append(round(b,3))\n",
    "    \n",
    "capm['alpha']=alpha\n",
    "capm['beta']=beta\n",
    "capm.index=rets.columns[:3]\n",
    "#输出结果：\n",
    "capm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>夏普比率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>1.404549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工商银行</th>\n",
       "      <td>-1.052569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国平安</th>\n",
       "      <td>-0.612243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>沪深300</th>\n",
       "      <td>-0.212937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           夏普比率\n",
       "贵州茅台   1.404549\n",
       "工商银行  -1.052569\n",
       "中国平安  -0.612243\n",
       "沪深300 -0.212937"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 超额收益率以无风险收益率为基准 假设无风险收益率为年化3%\n",
    "ex_return=rets - 0.03/250\n",
    "\n",
    "# 计算夏普比率\n",
    "sharpe_ratio=np.sqrt(len(ex_return))*ex_return.mean()/ex_return.std()\n",
    "sharpe_ratio=pd.DataFrame(sharpe_ratio,columns=['夏普比率'])\n",
    "sharpe_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>信息比率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>2.124507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>工商银行</th>\n",
       "      <td>-0.740165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国平安</th>\n",
       "      <td>-0.672339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          信息比率\n",
       "贵州茅台  2.124507\n",
       "工商银行 -0.740165\n",
       "中国平安 -0.672339"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###信息比率\n",
    "ex_return = pd.DataFrame() \n",
    "ex_return['贵州茅台']=rets.iloc[:,0]-rets.iloc[:,3]\n",
    "ex_return['工商银行']=rets.iloc[:,1]-rets.iloc[:,3]\n",
    "ex_return['中国平安']=rets.iloc[:,2]-rets.iloc[:,3]\n",
    "\n",
    "#计算信息比率\n",
    "information_ratio = np.sqrt(len(ex_return))*ex_return.mean()/ex_return.std()\n",
    "#信息比率的输出结果\n",
    "information_ratio = pd.DataFrame(information_ratio,columns=['信息比率'])\n",
    "information_ratio"
   ]
  }
 ],
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